Sino-US English Teaching, September 2022, Vol. 19, No. 9, 332-337 doi:10.17265/1539-8072/2022.09.006
Engagement Resources Across Disciplines in Rearch Articles:
A Corpus-Bad Study
SUN Fengjing, ZHANG Le
University of Shanghai for Science and Technology, Shanghai, China
This paper explores cross-disciplinary variations of engagement resources in rearch articles. Bad on the 884,557-word corpora of four typical disciplines, manual annotation is proceeded to mark engag
ement resources. After elaborated analys, four kinds of disciplines show different preferences for using engagement resources both in dialogical contraction and dialogical expansion, providing pedagogical implications for academic writing. Keywords: engagement, cross-disciplinary variations
Introduction
Engagement is the way to make different voices achieve communication in a certain speech community. In
the early stage, studies of the engagement system of Appraisal Theory (Martin & White, 2005) are mostly adopted by scholars to investigate news reports. Over the last 20 decades, making academic text analys by the framework of the engagement system has gradually received attention (Mei, 2007; Loghmani, Ghonsooly, & Ghazanfari, 2020). In another aspect, there has been a growing interest in disciplinary studies in academic discours. Earlier studies are concerned with only one discipline, but nowadays cross-disciplinary variations are noticed by veral scholars. However, existent cross-disciplinary studies related to engagement all adopt Hyland’s (2005) reader-oriented view, and few studies ponder cross-disciplinary variations bad on Martin and White’s (2005) engagement framework.
In this rearch, manual annotations of engagement resources by UAM Corpus Tool are firstly marked in the subcorpora of Corpora of Chine-English Academic Papers—CCEAP (Zhang, 2021) which is full of linguistic data we needed in the study. Then data are extracted to analyze how native English academic writers u engagement resources in different disciplines. In addition, different properties of four kinds of disciplines will be considered as well. Through this study, the rearcher hopes to reveal the situation of engagement resources using four-discipline papers and find the cross-disciplinary variations in engagement. In addition, pedagogical implications for academic writing are also wished by this study.
Literature Review
Cross-Disciplinary Studies of Academic Discour
Wells (1992) mentioned that each discipline finally generates its own modes of discour. Indeed, Hyland
SUN Fengjing, Master’s degree, College of Foreign Languages, University of Shanghai for Science and Technology, Shanghai, China. ZHANG Le, Ph.D., associate professor, College of Foreign Languages, University of Shanghai for Science and Technology, Shanghai, China.
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A VID PUBLISHING
ENGAGEMENT RESOURCES ACROSS DISCIPLINES IN RESEARCH ARTICLES 333
(2015) adopted a corpus approach to demonstrate disciplinary preferences encoded in repeated patterns. In addition, Kaufhold and Mcgrath (2019) prented that the role of discipline triggers the epistemological or methodological, structural, and individual effects, which play key roles in writing for publication. Hence, for the sake of realizing more crets in academic writing, it is esntial to rearch with an eye to disciplinary studies. In the early stage, scholars mostly focud on a single discipline to explore the academic writing style (Mcgrath & Kuteeva, 2012). Nowadays, there has been a growing interest in cross-disciplinary rearch. The rearch subjects are diver, mostly concentrating on structural organization and specific syntactical realizations (Hu & Wang, 2014; Benelhadj, 2019). However, a few scholars work on the interpersonal function comparison across disciplines (Zou & Hyland, 2019; Dontcheva-Navratilova, 2021).
Engagement System in the Appraisal Theory
The Appraisal Theory is considered as the extension of the interpersonal function of Systemic Functional Linguistics. In retrospect to the theoretical background of the Appraisal Theory, Bakhtin’s (1981) dialogue theory has far-reaching influence. He emphasizes that meaning appears through dialogue at any level which the dialogue happens, which expands the linguistic study from formalism to a larger scope. Inspired by such theory, Martin and White’s (2005) Appraisal Theory considers systems as the center, appraisal as the focus, and the language as the technique to identify and analyze writers’/speakers’ stances, attitudes, and views. That is, the appraisal is a kind of analysis at the deep level through the surface level of meaning. The appraisal framework is divided into three subsystems: attitude, graduation, and engagement. The engagement system (shown in Figure 1) systematically explains how different voices communicate linguistically within a specific discoursal community.
Figure 1. The engagement system (Martin & White, 2005).
In prior rearch, veral analysts have adopted the framework of the engagement system in the Appraisal Theory to explore the authorial stance and the interpersonal relationship between readers and writers in academic discours (Mei, 2007; Loghmani et al., 2020). Some scholars have also delved into the disciplinary variations of academic papers in the field of engaging but utilize Hyland’s engagement framework (Zou & Hyland, 2019; Dontcheva-Navratilova, 2021). Martin’s Appraisal Theory ems like the concept of “Stance” by Hyland (2005) and becomes author-oriented, different from Hyland’s reader-oriented engagement. However, there is no study to investigate cross-disciplinary variations in academic papers by the engagement system in Martin’s view. The whole study aims to answer one rearch question: How do native English academic writers u engagement
resources in different disciplines?
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334
Methodology
The Corporanipple是啥意思
The corpora adopted in this study all come from Corpora of Chine-English Academic Papers—CCEAP (Zhang, 2021), consisting of two sub-corpora: the corpus of Chine academic papers and the corpus of English academic papers, providing abundant linguistic data to proceed with this study and English papers are mostly written by native English speakers according to the deduction of background information of writers. In order to better distinguish disciplinary differences, this rearch follows the classification of disciplines (Becher & Trowler, 2001). Disciplines are categorized into four kinds: hard pure, hard applied, soft pure, and soft hard. Hard disciplines mainly explore the physical environment with their atomistic knowledge. Knowledge develops cumulatively and is gained by empirical activities. Soft disciplines tend to investigate social phenomena with holistic knowledge. People obtain knowledge by personal illustration and knowledge is reiterative. The other division is pure & applied. Knowledge in pure disciplines is inclined to be universal, explanatory, and stable, whereas that in applied disciplines is apt to be utilitarian, individual, pragmatic, and unstable. Bad on the characteristics, the author choos 20 papers respectively in four disciplines reprentative as our linguistic data of soft-pure, soft-applied, hard-pure, and hard-applied: linguistics, education, physics, and electronics. The four sub-corpora in this rearch have 324,299 tokens in linguistics, 219,343 tokens in education, 175,213 tokens in physics, and 165,702 in electronics, respectively.
Data Analysis
Endeavoring to make results more accurate in this rearch, the author decided to utilize UAM Corpus Tool to annotate the linguistic data bad on the engagement system considering there is no accurate enough corpus arching method to capture the engagement features. Then, standardized frequencies per 1,000 words of every engagement resource in each discipline are computed to compare differences and similarities. Chi-square values and p-value are also considered to judge if the results are significant.
Results
Table 1 indicates an overview of dialogical expansion and contraction among four disciplines. Table 2 prents a generalization of two categories. Firstly, papers of four disciplines all adopt dialogical contraction more frequently than dialogical expansion, which reflects certain qualities of academic discour. That is, academic authors write articles in order to find some new points in this field and tend to utilize some techniques to restrict the dialogue space to make their own opinions salient. In addition, the proportion of contraction resources ems to be even except for physic considering physicists lay more emphasis on their own voices or the correctness of their theories in the process
of academic writing. Furthermore, writers em to u more implicit pronouncements than explicit ones, reflecting the implicitness of academic papers. Next, granular comparisons of detailed engagement resources of soft & hard disciplines and pure & applied disciplines would proceed in the following parts.
ENGAGEMENT RESOURCES ACROSS DISCIPLINES IN RESEARCH ARTICLES 335
Table 1
Overall Frequencies of Dialogical Expansion and Contraction (the Upper Data is Raw Data, While Standardized Frequency (per 1,000 Words) Is Prented Below Raw Data)
Linguistics Education Physics Electronics
Contraction Disclaim
Denial
2,238
6.90
1,640
7.48
626
3.57
937
5.66
Counter
3,444
10.62
2,213
10.09
1,158
6.61
1,139
6.87 Proclaim
Concur
220
0.68
82
0.37新东方培训
98
0.56
45
0.27
Endor
590
1.82
290
1.32
434
2.48
268
1.62
Pronounce
Explicit
192
0.59
86
0.39
122
0.70
minolta
65
0.39
Implicit
建模方法
478
1.47
348
1.59
218
1.24
160
0.97
Expansion Entertain
Evidence
281
0.87
50
better in time0.23
75
shithole0.43
162
0.98
Likelihood
2,202
6.80
1,670
7.33
516
2.95
748
4.51 Attribute
Acknowledge
lady gaga alejandro877
2.70
625
2.85
194
conquence
1.11
432
2.61
Distance拼搏的英文
48
0.15
15module1
0.07
70
0.40
56
0.34
Table 2
Generalization of Dialogical Contraction and Expansion
Dialogical contraction Percentage (%) Dialogical expansion Percentage (%)
Linguistics 7,162
22.09
67.76
3,408
10.51
32.24
Education 4,659
21.24
66.38
2,360
10.76
33.62
Physics 2,656
15.16
75.65
855
4.88
24.35
Electronics 2,614
15.78
65.15
1,398
8.44
34.85
Dialogical Contraction
Figure 2 shows the distribution of contraction engagement resources across four disciplines. It is obvious that academic writers of four disciplines are inclined to adopt “disclaim” rather than “proclaim” to highlight their own opinions. A large number of disclaiming engagement resources reflect that making contrasts with alternative voices ems more persuasive for authors themlves. In the subsystem of “disclaim”, people utilize more “counter-expectation” than “denial”, which can be explained by the degree of heteroglossia. “Counter-expectation” makes more allowances for alternati
ve voices than “denial”, so frequent u of it will make academic writers believe that their articles have constructed dialogue spaces to interact with other scholars and offer authors confidence to outline their own views. In the category of “disclaim”, “counter-expectation” accounts for a larger proportion, while among categories of “proclaim”, “endor” is mostly ud to prove some extra-vocalization is valid and acceptable and connect the voices with the authorial subjectivity.
For disciplines with pure-applied division (physics & electronics and linguistics & education), the overall trend is that papers of the hard applied discipline utilize more “disclaim” (for physics & electronics, ꭓ2 = 41.68, p < 0.01), whereas tho of pure disciplines adopt more “proclaim” (for physics & electronics, ꭓ2 = 61.46, p <
ENGAGEMENT RESOURCES ACROSS DISCIPLINES IN RESEARCH ARTICLES
336
0.01, while for linguistics & education, ꭓ2 = 24.149, p< 0.01). In this ca, reasons can be traced to the disciplinary features. The interpretability and stability of pure disciplines determine that the papers should be written on the basis of theorems and lf-evident truths which are reprented as “proclaim” considering “proclaim” tries to exclude and overwhelm contradictions to limit the range of
dialogue and prent common values that are universal and accepted. By the way, linguistics (a pure discipline) papers would like to overu “counter-expectation” (ꭓ2 = 3.53, p < 0.05), which violates the overall inclination. In addition, for disciplines with soft-hard division (physics & linguistics and electronics & education), the rule is much easier: Soft disciplines employ more engagement resources in every category of dialogical contraction than hard disciplines, which may be related to the reiterative property of soft disciplines. Array Figure 2. Distribution of contraction engagement resources in disciplines.
Dialogical Expansion
Here is the distribution of expansion engagement resources across four disciplines in Figure 3. Generally, writers appear to utilize more “entertain” than “attribute” in four disciplines. In the subsystem of “entertain”, “likelihood” is favored, while in the subsystem of “attribute”, people adopt “acknowledgment” much more than “distance”. The results reflect that people’s preference for “entertain” to show his/her opinion is only one of the possible suggestions; thus he/she dialogically makes the subjectivity of their points more explicit and leaves space for other possibilities. In addition, “acknowledgment” by using indirect or direct reported thought to express the authors’ neutral stance is obvious to connect exterior voices with authorial voices, in other words, which helps
to construct a n of community and promote the interactive force in the academic community.
For disciplines under the soft-hard division (physics & linguistics and electronics & education), both “entertain” and “attribute” are much more employed in soft disciplines than in hard disciplines. Particularly, engagement resources of “distance” such as the word “claim” exist more in hard disciplines, which reflects hard disciplines tend to dissociate writer’s voices with the responsibility of reporting such external voices. Other categories are mostly utilized more in soft disciplines. It is surprising that “evidence” is overud in the applied hard discipline (ꭓ2 = 95.06, p< 0.01) to enhance the opportunity of solidarity with tho similar practical guidelines. Furthermore, in terms of disciplines under the pure-applied division (physics & electronics and linguistics & education), engagement resources of “likelihood” are overud in applied disciplines to ensure the accuracy of academic writing, especially in the ction of the applicating process (for physics & electronics, ꭓ2 = 56.34, p < 0.01, while for linguistics & education, ꭓ2 = 12.44, p < 0.01), while “distance” is more utilized in pure disciplines (for linguistics & education, ꭓ2 = 6.49, p < 0.05), reflecting the strictly objective requirement in pure disciplines. By the way, authors of the hard applied discipline adopt more “entertain” and “attribute”, while
the standardized frequencies of the two subsystems in papers between the soft applied discipline
and pure
discipline are almost the same.
